Cardiac Monitoring

ABSTRACT

Systems and techniques for monitoring cardiac activity. In one aspect, a method includes collecting information describing the variability in heart rate over a series of beats, designating variability at a lower end of physiological values as being largely irrelevant to atrial fibrillation, designating variability in a midrange of physiological values as being indicative of atrial fibrillation, designating variability in an upper range of physiological values as being negatively indicative of atrial fibrillation, and determining a relevance of the variability described in the collection to atrial fibrillation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of U.S. application Ser. No.10/762,887, filed on Jan. 21, 2004, now U.S. Pat. No. ______ as acontinuation application. The contents of U.S. application Ser. No.10/762,887 are incorporated herein by reference.

BACKGROUND

The following description relates to cardiac monitoring, for example, bymonitoring cardiac electrical activity.

The electrical activity of the heart can be monitored to track variousaspects of the functioning of the heart. Given the volume conductivityof the body, electrodes on the body surface or beneath the skin oftendisplay potential differences related to this activity. Anomalouselectrical activity can be indicative of disease states or otherphysiological conditions that can range from benign to deadly.

One example of such a physiological condition is atrial fibrillation.Atrial fibrillation involves the loss of synchrony between the atria andthe ventricles. In complex atrial fibrillation, long-lived wavelets ofdepolarization travel along circular paths in the atria. This can leadto irregular ventricular beating as well as blood stagnation andclotting in the atria.

Atrial fibrillation is among the most common forms of cardiac arrhythmiaand may affect more than two million people annually. Atrialfibrillation has been associated with stroke, congestive heart failure,and cardiomyopathy.

Another example of such a physiological condition is atrial flutter.Atrial flutter also involves the loss of synchrony between the atria andthe ventricles. In atrial flutter, multiple atrial waveforms reach theatrioventricular (AV) node during each ventricular beat due to, e.g.,atrial scars, an atrial infarction, or a re-entrant circuit encircling aportion of the right atrium.

Atrial flutter is less common than atrial fibrillation but is alsoassociated with stroke, congestive heart failure, and cardiomyopathy.

SUMMARY

The cardiac monitoring systems and techniques described here may includevarious combinations of the following features.

A method can include determining a beat-to-beat variability in cardiacelectrical activity; determining a relevance of the variability to oneof atrial fibrillation and atrial flutter using a non-linear statistics,identifying one of an atrial fibrillation event and an atrial flutterevent based on the determined relevance. The event is a period in timewhen the information content of the cardiac electrical activity is ofincreased relevance.

The end of the event can be identified based on the determinedrelevance. An event state associated with atrial fibrillation can betransitioned into in response to identification of the event. The eventcan be transmitted to a remote receiver from an ambulatory patient. Therelevance of the variability to atrial fibrillation can be determined byreceiving information identifying a ventricular beat and assigning apreset value indicating that the variability is negatively indicative ofatrial fibrillation.

A ventricular tachycardia event can be identified based at least in parton the information identifying the ventricular beat. The relevance ofthe variability to atrial fibrillation can be determined by determiningan average relevance of variability in a collection of R to R intervals.

The beat-to-beat variability can be determined in a series of successivebeats, e.g., by determining the variability in an interval betweensuccessive R-waves. The event can be identified by comparing therelevance of the variability to a first predetermined amount ofrelevance. Further, the relevance of the variability in the event can becompared to a second predetermined amount of relevance to identify theend of the event. The second predetermined amount can be lower than thefirst predetermined amount.

A method can include collecting information describing the variabilityin heart rate over a series of beats, designating variability at a lowerend of physiological values as being largely irrelevant to atrialfibrillation, designating variability in a midrange of physiologicalvalues as being indicative of atrial fibrillation, designatingvariability in an upper range of physiological values as beingnegatively indicative of atrial fibrillation, and determining arelevance of the variability described in the collection to atrialfibrillation.

The variability can be designated by multiplying the informationdescribing the variability by a weighting factor. Information describinga variability in R to R intervals over a series of beats can becollected. The collected information can be a function of a ratio of afirst R to R interval and an immediately preceding R to R interval, suchas information related to factor DRR(n) as given by${D\quad R\quad{R(n)}} = {{{ABS}\left( {\frac{R\quad{R\left( {n,{n - 1}} \right)}}{{R\quad{R\left( {n,{n - 1}} \right)}} + {R\quad{R\left( {{n - 1},{n - 2}} \right)}}} - \frac{1}{2}} \right)}.}$

The variability at the lower end of physiological values can bedesignated as being largely irrelevant by designating informationrelated to factors DRR(n) less than about 0.0.2 as being largelyirrelevant. The variability at the midrange of physiological values canbe designated as being indicative of atrial fibrillation by designatinginformation related to factors DRR(n) greater than about 0.02 and lessthan about 0.15 as being indicative of atrial fibrillation. Thevariability at the upper range of physiological values can be designatedas being negatively indicative of atrial fibrillation by designatinginformation related to factors DRR(n) greater than about 0.157 as beingnegatively indicative of atrial fibrillation.

Information describing the variability can be collected by collectingthe variability in heart rate over a series of between 20 and 200 of therecent R to R intervals. The determined relevance of the variability canbe the relevance of the variability to sustained atrial fibrillation.The series of R to R intervals can be a continuous series of R to Rintervals.

A method can include comparing recent R to R intervals with preceding Rto R intervals to yield a collection of comparisons, weighting thecomparisons according to a likelihood that the comparisons are relevantto atrial fibrillation, and determining the average relevance of thecollection to atrial fibrillation. The weighting can include identifyinga first of the recent beats as a ventricular beat and assigning a presetvalue to weight the first beat in the collection. The preset value canbe negatively indicative of atrial fibrillation.

The comparisons can be weighted by designating variability at a lowerend of physiological values as being largely irrelevant to atrialfibrillation and designating variability in a midrange of physiologicalvalues as being indicative of atrial fibrillation. The comparisons canalso be weighted by designating variability in an upper range ofphysiological values as being negatively indicative of atrialfibrillation. A ventricular tachycardia event can be identified based atleast in part on the identification of the ventricular beat. Recent R toR intervals can be compared with immediately preceding R to R intervalsto yield a collection of comparisons.

The cardiac monitoring systems and techniques may provide one or more ofthe following advantages. Atrial fibrillation (“AFib”) and/or atrialflutter (“AFlut,” with “AF” referring to either) can be distinguishedfrom other types of cardiac arrhythmia, such as the normal sinus rhythmirregularity, irregularity from various types of heart blocks, and theirregularity associated with premature ventricular contractions. Thedescribed systems and techniques are a practical approach to calculatingthe beat-to-beat irregularity while providing improved positivepredictability of AF. Moreover, the described systems and techniques areable to identify sustained AF episodes, where AF continues for more thatapproximately 20 beats and has an increased clinical significance.

For example, when the systems and techniques described here were used toanalyze the MIT-BIH arrhythmia database, available from MIT-BIH DatabaseDistribution, MIT Room E25-505A, Cambridge, Mass. 02139, USA, asensitivity to AF in excess of 90% and a positive predictivity in excessof 96% were obtained.

The described systems and techniques are well-adapted to monitoringcardiac signals of ambulatory patients who are away from controlledenvironments such as hospital beds or treatment facilities. The cardiacsignals obtained from to ambulatory patients may be noisier andotherwise strongly impacted by the patients' heightened levels ofactivity. Thus, improved monitoring systems and techniques, such asthose described herein, are required for ambulatory patients.

The described systems and techniques are also well-adapted to real-timemonitoring of arrhythmia patients, where minimal delays indistinguishing between different types of cardiac arrhythmia can speedthe delivery of any urgent medical care. The described systems andtechniques also require minimal computational resources. Further, thedescribed systems and techniques do not require training beforedifferent types of cardiac arrhythmia can be distinguished.

The details of one or more implementations of the invention are setforth in the accompanying drawings and the description below. Otherfeatures, objects, and advantages will be apparent from the descriptionand drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 shows a system in which a cardiac signal is monitored for medicalpurposes.

FIG. 2 shows an example of a cardiac signal.

FIG. 3 shows an example of instrumentation for cardiac monitoring usinga cardiac signal.

FIG. 4 shows an example state diagram of a cardiac monitoring systemduring cardiac monitoring.

FIG. 5 shows a process for cardiac monitoring for the detection of an AFevent.

FIG. 6A shows a process for determining the variability in the recent Rto R intervals and identifying if the variability is relevant to eitherthe onset or termination of AF.

FIG. 6B shows a graph of factor DRR(n) as a function ofRR(n−1,n−2)/RR(n,n−1).

FIG. 7 shows a transformation function for weighting the variability inthe timing of recent beats.

FIG. 8 shows an example of instrumentation for cardiac monitoring usingan electrocardiogram trace.

FIG. 9 shows an example state diagram of a cardiac monitoring systemthat accommodates the variability caused by ventricular beats.

FIG. 10 shows a process for determining the variability of recent R to Rintervals and identifying if the variability is relevant to the onset ofAF while accommodating the variability caused by ventricular beats.

FIG. 11 shows a process for determining the variability in recent R to Rintervals and identifying if the variability is relevant to thetermination of AF while accommodating the variability caused byventricular beats.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

FIG. 1 shows a system 100 in which a cardiac signal is monitored formedical purposes. System 100 includes an individual 105, instrumentation110, a signal path 115, and a receiver 120. Individual 105 can be apatient or a healthy individual for whom monitoring of one or morebiological signals is deemed to be appropriate. Instrumentation 10 caninclude one or more sensing, calibration, signal processing, control,data storage, and transmission elements suitable for generating andprocessing the cardiac signal, as well as relaying all or a portion ofthe cardiac signal over path 115. Path 115 can be any suitable mediumfor data transmission, including wired and wireless media suitable forcarrying optical and/or electrical signals. The receiver 120 can includea receiver element for receiving the transmitted signal, as well asvarious data processing and storage elements for extracting and storingthe information carried by the transmission regarding the state ofindividual 105. The receiver 120 can be a medical system in thatreceiver 120 presents information to medical personnel or to a medicalexpert system for analysis. The receiver 120 either can reside remotelyfrom instrumentation 110 in that receiver 120 is not located at the samesite as instrumentation 110 (e.g., at the same hospital, nursing home,or other medical care facility) or the receiver 120 can reside withinthe same general area or vicinity as instrumentation 110 (e.g., withinthe same room, building, or health care facility).

FIG. 2 shows an example of a cardiac signal, namely the trace of ascalar electrocardiogram 200. Electrocardiogram trace 200 follows apotential difference 205 measured between two points on the body surfaceof an individual. Potential difference 205 changes with time 210 in amanner characteristic of the physiology and function of an individual'sheart.

Electrocardiogram trace 200 generally includes features characteristicwith particular aspects of cardiac activity. For example, trace 200includes a series of QRS complexes 215, 220, 225 associated withactivation of the ventricles. QRS complex 225 includes an R-wave R_(n),QRS complex 220 includes an R-wave R_(n−1), and QRS complex 215 includesan R-wave R_(n−2). The time between successive R-waves can be referredto as the R to R interval. In particular, the R to R interval betweenR-wave R_(n) and R-wave R_(n−1) is RR(n,n−1) and the R to R intervalbetween R-wave R_(n−1) and R-wave R_(n−2) is RR(n−1,n−2).

FIG. 3 shows an example of instrumentation 110 for cardiac monitoringusing a cardiac signal such as electrocardiogram trace 200.Instrumentation 110 includes a sensor 305, a signal amplifier/processor310, a beat detector 315, an atrial fibrillation/atrial flutter (AF)detector 320, decision logic 325, and an event generator 330. Sensor 305can include two or more electrodes subject to one or more potentialdifferences that yield a voltage signal such as electrocardiogram trace200. The electrodes can be body surface electrodes such as silver/silverchloride electrodes and can be positioned at defined locations to aid inmonitoring the electrical activity of the heart. Sensor 305 can alsoinclude leads or other conductors that form a signal path to signalamplifier/processor 310. Signal amplifier/processor 310 can receive,amplify, and/or process the voltage signals. The processing can includefiltering and digitization. The amplification and remainder of theprocessing can occur before or after digitization. Signalamplifier/processor 310 can provide the amplified and/or processedsignal to beat detector 315.

Beat detector 315 is a device such as a circuit or other arrangementthat identifies the time period between ventricular contractions. Forexample, beat detector 315 can be a QRS detector in that it identifiessuccessive QRS complexes (or an equivalent indicator of ventricularactivity) and determines the beat-to-beat timing from the time betweencomplexes. The beat-to-beat timing can be determined by measuring timesbetween successive R-waves, such as RR(n,n−1) and RR(n−1,n−2) inelectrocardiogram trace 200 (FIG. 2). Beat detector 315 can provideinformation regarding the time period between ventricular contractionsto AF detector 320.

AF detector 320 is a data processing device that analyzes informationregarding the time period between ventricular contractions to detect AF.The detection of AF can include distinguishing AF from other sources ofventricular irregularity, such as premature ventricular contraction,heart blocks, and normal sinus rhythm irregularity. The detection of AFcan also include distinguishing between short AF episodes and sustainedAF episodes. Short AF episodes generally include between two and 20beats and may or may not have clinical significant, whereas sustained AFepisodes generally include more than 20 beats and may have relativelygreater clinical significance. The detection of AF can also include thedetection of other types of irregularity caused by random refractoryperiods of the ventricles.

AF detector 320 can analyze information regarding the time periodbetween ventricular contractions to detect AF using non-linearstatistical approaches. Non-linear statistics treats the relationshipbetween variables as something other than a linear function. Detailregarding an example non-linear statistical approach to detecting AF isgiven below. AF detector 320 can provide information regarding thedetection of AF to decision logic 325

Decision logic 325 is a set of instructions for determining when the AFdetected by AF detector 320 has commenced and terminated. For example,decision logic 325 can be embodied in a circuit or decision logic 325can be executed by a data processing device such as AF detector 320.Decision logic 325 can also trigger the generation of an AF event byevent generator 230.

Event generator 330 is a device such as a data processing device thatprepares an AF event for handling. An AF event is a period in time whenthe information content of the signal sensed by sensor 305 is deemed tobe of increased relevance to the monitoring of AF. AF events need not beof equal or predetermined duration. For example, an event associatedwith an sustained AF episode may have a longer duration than an eventassociated with a short AF episode.

Event generator 330 can prepare an AF event for handling by collectinginformation that summarizes the relevance of the event to the detectionand/or monitoring of AF. For example, event generator 330 can excisedata associated with the period identified as AF from the amplified andprocessed signal output from signal amplifier/processor 310. Eventgenerator 330 can also redact such data (e.g., by selecting the firstthree minutes worth when generating the event). Handling the AF eventcan include transmitting the AF event over data link 115 or storing theAF event in a data storage device.

FIG. 4 shows an example state diagram 400 of a cardiac monitoring systemduring cardiac monitoring. For example, state diagram 400 can relate tothe operation of an assembly such as AF detector 320 and decision logic325 in instrumentation 110 (FIG. 3). State diagram 400 includes an idlestate 405 and an AF event state 410. Idle state 405 originates areflexive transition 415 and a state transition 420. AF event state 410originates a reflexive transition 425 and a state transition 430.Reflexive transition 415 is associated with a series of variabilitymeasurements. State transition 420 is triggered by the onset of AF-typevariability as detected by such measurements. Reflexive transition 425is associated with another series of variability measurements. Statetransition 430 is triggered by the end of AF-type variability asdetected by such measurements.

In operation, a cardiac monitoring system can start in idle state 405and measure the variability of a cardiac signal. For example, the systemcan measure the variability in the beat-to-beat timing of successiveR-waves, such as the variability between RR(n,n−1) and RR(n−1,n−2) inelectrocardiogram trace 200 (FIG. 2). Once the variability has beenidentified as AF-type variability, the system transitions to AF eventstate 410 where the system continues to measure the variability of thecardiac signal. In AF event state 410, once the AF-type variability hasended, the system returns to idle state 405.

FIG. 5 shows a process 500 for cardiac monitoring, e.g., for thedetection of an AF event. Process 500 can be performed by one or moredata processing devices that perform data processing activities. Theactivities of process 500 can be performed in accordance with the logicof a set of machine-readable instructions, a hardware assembly, or acombination of these and/or other instructions. The activities inprocess 500 can be performed at any of a number of different elements ina system in which a biological signal is monitored. For example, ininstrumentation 110 (FIG. 3), the activities in process 900 can beperformed at AF detector 320, decision logic 325, and event generator330.

The device performing process 500 receives information regarding thetiming of recent beats it 505. The timing information can be received indiscrete amounts (e.g., on a beat-to-beat basis) or in a collection thatincludes such information. Using the received timing information, thesystem determines the variability in the recent R to R intervals at 510.The variability in the R to R intervals can reflect the beat-to-beatchange in heart rate over a set period or over a set number of beats.

The system can also identify the relevance of such variability to AF at515. The variability is relevant to AF when it is associated with a highprobability that an individual undergoes AF at or near the time of therecent beats. Relevance can be identified by comparing the variabilityto a predetermined amount of variability or to an amount identified astypical for the monitored patient.

The system can also determine if the identified relevance of thevariability is indicative of the monitored individual undergoing AF atdecision 520. If not, the system returns to 505. This return cancorrespond to the system remaining in idle state 405 along reflexivetransition 415 in state diagram 400 (FIG. 4). If the system determinesthat the results of the monitoring are indicative of the individualundergoing AF, the system initiates an AF event at 525. This initiationof the AF event can correspond to the system transitioning to AF eventstate 410 in state diagram 400 (FIG. 4). The initiation of such an eventcan include various activities that lead to the generation of an event,such as triggering an event generator to add markers to a data streamsuch as electrocardiogram trace 200 or excising a relevant portion ofthe data stream.

The system can continue to receive information regarding the timing ofrecent beats at 530. Using the received timing information, the systemdetermines the variability in the recent R to R intervals at 535. Thesystem can also identify the relevance of such variability to the end ofAF at 540. The variability is relevant to the end of AF when it isassociated with an increased probability that AF has halted. Relevancecan be identified by comparing the variability to a predetermined amountof variability or to an amount identified as typical for the monitoredpatient.

The system can also determine if the identified relevance of thevariability indicates that AF has ended in the monitored individual atdecision 545. If not, the system returns to 530. This return cancorrespond to the system remaining in AF event state 410 along reflexivetransition 425 in state diagram 400 (FIG. 4). If the system determinesthat AF has ended in the monitored individual, the system returns to555. This return can correspond to the system transitioning to idlestate 405 in state diagram 400 (FIG. 4).

FIG. 6A shows a process 600 for determining the variability in therecent R to R intervals and identifying if the variability is relevantto either the onset or termination of AF. Process 600 can be performedindependently or process 600 can be performed as part of a largercollection of activities. For example, process 600 can be performed aspart of process 500, namely as steps 510, 515 or as steps 535, 540 (FIG.5). Various activities in process 600 can also be performed to triggerstate transitions 420, 430 in state diagram 400 (FIG. 4).

The system performing process 600 can compare the most recent R to Rinterval (e.g., RR(n,n−1) of FIG. 2) with the immediately preceding R toR interval (e.g., RR(n−1,n−2) of FIG. 2) at 605. Such a comparison canyield a factor that reflects the beat-to-beat variability in heart rate.For example, a factor DRR(n), given by the expression $\begin{matrix}{{D\quad R\quad{R(n)}} = {{ABS}\left( {\frac{R\quad{R\left( {n,{n - 1}} \right)}}{{R\quad{R\left( {n,{n - 1}} \right)}} + {R\quad{R\left( {{n - 1},{n - 2}} \right)}}} - \frac{1}{2}} \right)}} & {{Equation}\quad 1}\end{matrix}$can reflect the beat-to-beat variability in R to R interval and in heartrate. A graph of factor DRR(n) as a function of RR(n−1,n−2)/RR(n,n−1) isshown in FIG. 6B.

The system performing process 600 can also weight the comparison of themost recent R to R interval with the immediately preceding R to Rinterval according to the likelihood that the results of the comparisonare indicative of AT at 610. The weighting can determine a role that thecomparison will play in subsequent processing cardiac monitoringactivities. For example, the weighting can include the whole or partialexclusion of a certain comparisons from subsequent cardiac monitoringactivities.

One technique for weighting the comparison is through the use of atransformation, such as transformation function 700 shown in FIG. 7.Transformation function 700 provides weights that are multiplied by thevalue of a comparison (e.g., factor DRR(n)) to reflect the relevance ofthe comparison to AF. The weights provided in transformation function700 can be multiplied by the value of every comparison or by a selectedsubset of the comparisons. One technique for selecting such a subset isdiscussed further below.

Transformation function 700 is adapted to the factor DRR(n) given inequation 1. In particular, transformation function 700 is adapted tooverweight factor DRR(n) when factor DRR(n) is in a midrange ofpotential physiological values (e.g., when DRR(n) is greater than about0.0.2 and less than about 0.15). Transformation function 700 is adaptedto weight factor DRR(n) as being negatively indicative of AF when factorDRR(n) is at the upper range of potential physiological values (e.g.,when DRR(n) is greater than about 0.157). Transformation function 700 isadapted to weight factor DRR(n) as being largely irrelevant to AF whenfactor DRR(n) is at the lower range of potential physiological values(e.g., when DRR(n) is less than about 0.0.2). Transformation function700 includes a scalar weight 705 that varies as a function of thecomparison factor DRR(n) 710. In particular, weight 705 varies linearlybetween points 715, 720, 725, 730, 735. The values of points 715, 720,725, 730, 735 are given in Table 1. TABLE 1 Comparison Point DRR(n)Weight 715 0 0 720 0.0206 0.0417 725 0.0642 0.9178 730 0.1427 0.1005 7350.2 −0.3

In operation, weight 705 for any value of the factor DRR(n) can bedetermined by linear interpolation between the weights of points 715,720, 725, 730, 735. The interpolation can be performed for each value ofthe factor DRR(n) as it arises or the results of a certain number ofsuch interpolations can be stored in a look up table. For any value ofthe factor DRR(n) above 0.2, a weight of −0.3 can be assigned.

Returning to FIG. 6A, the system performing process 600 can also add aweighted comparison to a collection of weighted comparisons for recentbeats at 615. For example, the system can form a FIFO stack or an arrayof weighted comparisons having a separate data element for each ofbetween 10 and 200 (e.g., 100) of the most recent beats. The system canalso determine the relevance of the collection of weighted comparisonsfor recent beats to AF at 620. The collection of weighted comparisonscan be relevant to either the onset or termination of AF.

To determine the relevance, the system can sum the weighted comparisonsto arrive at a number that represents the average relevance of theweighted comparisons in the collection. The system can calculate suchsums for several beats in a row before determining that the beat-to-beatvariability is indicative of the onset or termination of AF. In oneimplementation, the system calculates the average of the weightedcomparisons of the beats in the collection and compares this averagewith a first predetermined threshold to determine if the variability isindicative of the onset of AF and with a second predetermined thresholdto determine if the variability is indicative of the termination of AF.In general, the first, onset threshold may be higher than the second,termination threshold. The difference between the onset and terminationthresholds can introduce hysteresis into the state transitions tostabilize any system performing process 600.

FIG. 8 shows an example of instrumentation for cardiac monitoring usingan electrocardiogram trace, namely instrumentation 800. In addition tosensor 305, signal amplifier/processor 310, AF (AF) detector 320,decision logic 325, and event generator 330, instrumentation 800 alsoincludes a QRS detector 805 and a ventricular beat detector 810. QRSdetector 805 and ventricular beat detector 810 can both receive anamplified and processed signal from signal amplifier/processor 310. QRSdetector 805 is a device such as a circuit or other arrangement thatidentifies the time period between successive QRS complexes. QRSdetector 805 can provide information regarding the time period betweensuccessive QRS complexes to AF detector 320

Ventricular beat detector 810 is a device such as a circuit or otherarrangement that identifies ventricular beats. Ventricular beats (i.e.,premature ventricular beats) are irregular beats that interrupt thenormal heart rhythm. Ventricular beats generally arise from aventricular focus with enhanced automaticity. Ventricular beats may alsoresult from reentry within the His-Purkinje system. The occurrence ofventricular beats is generally unrelated to AF. For example, theoccurrence of ventricular beats can be used to identify ventriculartachycardia (e.g., when there are three or more consecutive ventricularbeats). Ventricular beats may be precipitated by factors such asalcohol, tobacco, caffeine, and stress. Ventricular beat detector 810can monitor an electrocardiogram trace to identify ventricular beats.Various systems and techniques for identifying ventricular beats can beused. For example, the Mortara VERITAS Analysis Algorithm, availablefrom Mortara Instrument, Inc. (Milwaukee, Wis.), can be used.Ventricular beat detector 810 can also provide information regarding theoccurrence of ventricular beats to AF detector 320.

Ventricular beat detector 810 can be housed together with QRS detector805. An example of such a joint device is the ELI 250TMElectrocardiograph available from Mortara Instrument, Inc. (Milwaukee,Wis.).

Approaches for determining the variability in recent R to R intervalsand identifying if the variability is relevant to either the onset ortermination of AF can accommodate the variability caused by ventricularbeats. FIG. 9 shows an example state diagram 900 of a cardiac monitoringsystem that accommodates the variability caused by ventricular beats. Inaddition to idle state 405 and AF event state 410, state diagram 900also includes a ventricular tachycardia (V-TA CH) event state 905.Ventricular tachycardia is a rapid succession of ventricularcontractions (e.g., between 140 and 220 per minute) generally caused byan abnormal focus of electrical activity in a ventricle. Ventriculartachycardia can last from a few seconds to several days and can becaused by serious heart conditions such as a myocardial infarction. AFevent state 410 originates a state transition 910 that is triggered bythe occurrence of three consecutive ventricular beats. V-TACH eventstate 905 originates a state transition 910 that is triggered by the endof a V-TACH event. The end of a V-TACH event can be identified, e.g.,when the rate of ventricular contractions falls below a predeterminedvalue (e.g., a value between 100 and 200 bpm).

FIG. 10 shows a process for determining the variability in recent R to Rintervals and identifying if the variability is relevant to the onset ofAF while accommodating the variability caused by ventricular beats,namely a process 1000. Process 900 can be performed independently orprocess 1000 can be performed as part of a larger collection ofactivities. For example, process 1000 can be performed as part ofprocess 500, namely as steps 510, 515 (FIG. 5). Various activities inprocess 1000 can also be performed to trigger state transition 420 instate diagram 900 (FIG. 9).

The system performing process 1000 can compare the recent R to Rintervals with the respective, immediately-preceding R to R intervals at1005 using, e.g., the expression in Equation 1 to reflect thebeat-to-beat variability in heart rate. The system performing can alsoreceive an indicator of the occurrence of a ventricular beat at 1010.Such an indicator can be received, e.g., from a ventricular beatdetector.

The system can create an array or other data structure that includesboth the ventricular beat indicators and the R to R interval comparisonsat 1015. The array can include the ventricular beat indicators and the Rto R interval comparisons for between 10 and 200 (e.g., 100) of the mostrecent beats. The system can also weight the comparisons according tothe likelihood that the R to R interval comparisons are relevant to AFat 1020 using, e.g., transformation function 700 (FIG. 7).

The system can also assign a preset value to the R to R intervalcomparisons associated with ventricular beats at 1025. The preset valuecan be a penalty value in that the preset value reflects a decreasedlikelihood that the variability is indicative of an AF event. The presetvalue can be selected in light of the approaches used to compare the Rto R intervals and to weight such comparisons. For example, when the Rto R intervals are compared using Equation 1 and the resultingcomparisons are weighted using transformation function 700 (FIG. 7), Rto R interval comparisons associated with ventricular beats can beassigned a preset value of −0.06 and R to R intervals comparisonsassociated with the R to R intervals immediately succeeding ventricularbeats can be assigned a preset value of zero.

Using both the weighted and preset timing comparisons, the system cancalculate the average value of an entry in the array of the most recentbeats at 1030. If the system determines that the average is greater than0.22 for the last five beats at decision 1035, then the system triggersthe start of an AF event in the recent beats at 1040. On the other hand,if the system determines that the average is less than or equal to 0.22for the last five beats, then the system returns to compare the recent Rto R intervals with the previous R to R interval at 1005.

FIG. 11 shows a process for determining the variability in the recent Rto R intervals and identifying if the variability is relevant to thetermination of AF while accommodating the variability caused byventricular beats, namely a process 1100. Process 1100 can be performedindependently or process 1100 can be performed as part of a largercollection of activities. For example, process 1100 can be performed aspart of process 500, namely as steps 535, 540 (FIG. 5). Variousactivities in process 1100 can also be performed to trigger statetransitions 430, 910, 915 in state diagram 900 (FIG. 9).

The system performing process 1100 can perform the activities at 1005,1010, 1015, 1020, 1025, 1030 as in process 1000. The system can alsodetermine if the last three beats have been ventricular beats atdecision 1105. For example, the system can determine if the last threebeats are marked with a ventricular beat occurrence indicator such asthat received at 1010.

If the system determines that the last three beats have been ventricularbeats, the system triggers the end of the AF event at 1110 and, whenappropriate, terminates a ventricular tachycardia event at 1115. Thestart and termination of the ventricular tachycardia event cantransition the state of a system into and out of a V-TACH event, muchlike transitions 910, 915 in state diagram 900 (FIG. 9).

When the V-TACH event has been terminated at 1115 or when the systemdetermines that the last three beats have not been ventricular beats at115, the system then determines if the average of both the weighted andpreset timing comparisons in the array of the most recent beats hasdropped below 0.08 at decision 1120. If the average has not droppedbelow 0.08, the system returns to compare the recent R to R intervalswith the previous R to R interval at 1005. On the other hand, when theaverage has dropped below 0.08, the system triggers the end of the AFevent at 1125. This triggering can transition the state of a system outof an AF event, much like transition 430 in state diagram 900 (FIG. 9).

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include one or more computer programsthat are executable and/or interpretable on a programmable systemincluding at least one programmable processor, which may be special orgeneral purpose, coupled to receive data and instructions from, and totransmit data and instructions to, a storage system, at least one inputdevice, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) may include machine instructions for aprogrammable processor, and can be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the term “machine-readablemedium” refers to any computer program product, apparatus and/or device(e.g., magnetic discs, optical disks, memory, Programmable Logic Devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor, including a machine-readable medium thatreceives machine instructions as a machine-readable signal. The term“machine-readable signal” refers to any signal used to provide machineinstructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing environment that includes a back-end component (e.g., as adata server), or that includes a middleware component (e.g., anapplication server), or that includes a front-end component (e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the systemsand techniques described here), or any combination of such back-end,middleware, or front-end components. The components of the environmentcan be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a local area network (“LAN”), a wide area network(“WAN”), and the Internet.

The computing environment can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made. Cardiac signalsother than scalar electrocardiograms such as heart sounds can bemonitored. Other weighting approaches and transformation functions canbe used, depending upon the manner in which the timing of beats iscompared. Weight 705 can be interpolated in any of a number of differentways such as a cubic spline between points 715, 720, 725, 730, 735.Cardiac monitoring can be performed in real time or delayed. The valuesof different parameters can be changed and useful results stillobtained. For example, in FIG. 7, point 735 can be repositioned to acomparison factor DRR(n) value above 0.2. Accordingly, otherimplementations are within the scope of the following claims.

1. A device, comprising: a beat detector to identify a beat-to-beattiming of cardiac activity; a ventricular beat detector to identifyventricular beats in the cardiac activity; variability determinationlogic to determine a variability in the beat-to-beat timing of acollection of beats; relevance determination logic to identify arelevance of the variability in the beat-to-beat timing to at least oneof atrial fibrillation and atrial flutter; and an event generator togenerate an event when the variability in the beat-to-beat timing isidentified as relevant to the at least one of atrial fibrillation andatrial flutter in light of the variability in the beat-to-beat timingcaused by ventricular beats identified by the ventricular beat detector.2. The device of claim 1, wherein the relevance determination logic isto accommodate variability in the beat-to-beat timing caused byventricular beats by weighting ventricular beats as being negativelyindicative of the one of atrial fibrillation and atrial flutter.
 3. Thedevice of claim 1, wherein the variability determination logic is tocompare times between R-waves in three successive QRS complexes todetermine the variability in the beat-to-beat timing.
 4. The device ofclaim 1, wherein: the variability determination logic is to representthe variability in the beat-to-beat timing as a factor that is lowestwhen a first time between beats is close to a second time between beats;and the first time immediately proceeds the second time.
 5. The deviceof claim 4, wherein the variability determination logic is to representthe variability in the beat-to-beat timing as a factor that increasesnon-linearly when the absolute difference between the first time thesecond time grows.
 6. The device of claim 4, wherein the variabilitydetermination logic is to represent the variability in the beat-to-beattiming as a factor that increases more rapidly when the first time growsless than the second time than when the first time grows greater thanthe second time.
 7. The device of claim 1, wherein the event generatoris to generate an event by performing operations comprising: collectingdata associated with the collection of beats; and transmitting the dataassociated with the collection of beats to a remote receiver.
 8. Thedevice of claim 1, wherein the relevance determination logic comprisesweighting logic to: weight variability at a lower end of physiologicalvalues as being substantially irrelevant to the one of atrialfibrillation and atrial flutter; weight variability in a midrange ofphysiological values as being positively indicative of the one of atrialfibrillation and atrial flutter; and weight variability in an upperrange of physiological values as being negatively indicative of the oneof atrial fibrillation and atrial flutter.
 9. The device of claim 8,wherein the weighting logic is also to weight a beat identified as aventricular beat as being negatively indicative of the one of atrialfibrillation and atrial flutter.
 10. The device of claim 1, wherein therelevance determination logic comprises logic to identify the relevanceof the variability using a non-linear function of a beat-to-beatinterval.
 11. The device of claim 1, wherein the beat detector comprisesa QRS detector.
 12. The device of claim 1, further comprising a sensorthat includes two or more body surface electrodes subject to one or morepotential differences related to cardiac activity.
 13. A methodcomprising: receiving information describing a timing of heart beats ofan individual; determining a first time between a first heart beat and asecond heart beat of the individual, wherein the second heart beatfollows immediately after the first heart beat; determining a secondtime between the second heart beat and a third heart beat of theindividual, wherein the third heart beat follows immediately after thesecond heart beat; determining a factor reflecting the differencebetween the first time and the second time, wherein the factor is lowestwhen the first time is close to the second time, and the factorincreases non-linearly when the absolute difference between the firsttime the second time grows; and identifying at least one of an atrialfibrillation event and an atrial flutter event of the individual basedon the factor.
 14. The method of claim 13, wherein the factor increasesmore rapidly when the first time grows less than the second time thanwhen the first time grows greater than the second time.
 15. The methodof claim 13, wherein: the method further comprises weighting the factorto reflect a relevance of the factor to one of atrial fibrillation andatrial flutter; and the identifying of the at least one of the atrialfibrillation event and the atrial flutter event is based on the weightedfactor.
 16. The method of claim 15, wherein weighting the factorcomprises: weighting the factor at a lower end of physiological valuesas being substantially irrelevant to the one of atrial fibrillation andatrial flutter; weighting the factor in a midrange of physiologicalvalues as being positively indicative of the one of atrial fibrillationand atrial flutter; and weighting the factor in an upper range ofphysiological values as being negatively indicative of the one of atrialfibrillation and atrial flutter.
 17. The method of claim 13, wherein:the method further comprise repeating the determining of the first time,the determining of the second time, and the determining of the factorfor additional heart beats to generate additional factors; and theidentifying of the at least one of the atrial fibrillation event and theatrial flutter event is based on the additional factors.
 18. The methodof claim 17, wherein identifying the at least one of the atrialfibrillation event and the atrial flutter event of the individual basedon the additional factors comprises identifying the at least one of theatrial fibrillation event and the atrial flutter event of the individualbased on between 19 and 199 additional factors.
 19. The method of claim13, wherein determining the factor comprises determining DRR(n) as givenby${D\quad R\quad{R(n)}} = {{{ABS}\left( {\frac{R\quad{R\left( {n,{n - 1}} \right)}}{{R\quad{R\left( {n,{n - 1}} \right)}} + {R\quad{R\left( {{n - 1},{n - 2}} \right)}}} - \frac{1}{2}} \right)}.}$20. An article comprising one or more machine-readable media storinginstructions operable to cause one or more machines to performoperations, the operations comprising: determining a beat-to-beatvariability in cardiac electrical activity; determining a relevance ofthe variability over a collection of beats to one of atrial fibrillationand atrial flutter using a non-linear function of a beat-to-beatinterval; and identifying one of an atrial fibrillation event and anatrial flutter event based on the determined relevance, the event beinga period in time when the information content of the cardiac electricalactivity is of increased relevance to the one of atrial fibrillation andatrial flutter.
 21. The article of claim 20, wherein determining therelevance comprises: weighting variability at a lower end ofphysiological values as being substantially irrelevant to the one ofatrial fibrillation and atrial flutter; weighting variability in amidrange of physiological values as being positively indicative of theone of atrial fibrillation and atrial flutter; weighting variability inan upper range of physiological values as being negatively indicative ofthe one of atrial fibrillation and atrial flutter; and determining arelevance of the weighted variability to the one of atrial fibrillationand atrial flutter.
 22. The article of claim 20, determining therelevance comprises: identifying a beat of the collection as aventricular beat, and weighting the beat as being negatively indicativeof the one of atrial fibrillation and atrial flutter.
 23. The article ofclaim 20, wherein: determining the beat-to-beat variability comprisesdetermining a factor reflecting the difference between a first timebetween a first heart beat and a second heart beat and a second timebetween a second heart beat and a third heart beat; the second heartbeat follows immediately after the first heart beat; and the third heartbeat follows immediately after the second heart beat.
 24. The article ofclaim 23, wherein: the factor is lowest when the first time is close tothe second time; and the factor increases non-linearly when the absolutedifference between the first time the second time grows.
 25. The articleof claim 24, wherein the factor increases more rapidly when the firsttime grows less than the second time than when the first time growsgreater than the second time.